The sentiment in a sentence or text reflects the overall positive, negative, or neutral opinion or thought of the person who produces or consumes it. It indicates whether a person is happy, unhappy, or neutral about the subject or context that describes the text. It can be quantified as a discrete value, such as 1 for happy, -1 for unhappy, and 0 for neutral, or it can be quantified on a continuous scale of values, from 0-1. Sentiment analysis, therefore, is the process of deriving this value from a piece of text that can be obtained from different data sources, such as social networks, product reviews, news articles, and so on. One real-world application of sentiment analysis is in social network data to derive actionable insights, such as customer satisfaction, product or brand popularity, fashion trends, and so on. The screenshot that follows shows one of the applications of sentiment analysis, in capturing the overall opinion of a particular news article about Google. The reader may refer to the application, or API, from Google Cloud at https://cloud.google.com/natural-language/:

The preceding screenshot indicates that sentiment data is captured for the whole document, as well as at the individual sentence level.